52 citations · 80 across the 48 of their papers we have counts for
6 papers · 1 filter
Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids
Hadi Nekoei, Alexandre Blondin Massé, Rachid Hassani +2
Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challenge in real-world settings, particularly…
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Hadi Nekoei, Aman Jaiswal, Patrice Bechard +7
Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tunin…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao, Tengyu Xu, Xuewei Wang +11
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one. One of the approaches for improving mat…
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs
Megh Thakkar, Quentin Fournier, Matthew Riemer +4
There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these…
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers
Amir Ardalan Kalantari, Mohammad Amini, Sarath Chandar +1
Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many curre…